Construction of Knowledge Base for Emergency Power Supply Guarantee Mechanism and Intelligent Decision Support System Based on Knowledge Graph

Main Article Content

Z. F. Zhang
Q. S. Li
R. R. Du
J. Wang
Y. Zhang
D. J. M. Yang

Abstract

This paper combines knowledge graph and intelligent decision-making technologies to conduct research on the construction of an emergency power supply guarantee knowledge base and an intelligent decision support system. Firstly, the paper sorts out the relevant theories and technologies of emergency power supply guarantee, knowledge graph and intelligent decision-making, completes domain ontology modeling, carries out collection, cleaning, knowledge extraction and fusion of multi-source heterogeneous data, and builds a standardized emergency power supply guarantee knowledge base. Secondly, adopting hybrid knowledge reasoning and multi-objective optimization algorithms, the overall design, function development and deployment of the intelligent decision support system are completed based on the front-end and back-end separation architecture. Finally, functional, performance and security tests are conducted, and a comparative analysis is carried out combined with a real-world case of power outage caused by a typhoon disaster. The results show that the system can realize the intelligent processing of the whole process including fault diagnosis, resource scheduling and emergency plan generation. Compared with the traditional manual mode, the emergency response speed, resource utilization rate and power supply reliability are significantly improved. This study can provide technical reference and practical experience for the intelligent construction of power emergency power supply guarantee.

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How to Cite
Zhang, Z. F., Li, Q. S., Du, R. R., Wang, J., Zhang, Y., & Yang, D. J. M. (2026). Construction of Knowledge Base for Emergency Power Supply Guarantee Mechanism and Intelligent Decision Support System Based on Knowledge Graph. Advanced Electromagnetics, 15(3), 10971–10979. https://doi.org/10.7716/aem.v15i3.4307
Section
Research Articles

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